Abstract
This paper describes the system that was submitted by DiDi Labs to the offline speech translation task for IWSLT 2020. We trained an end-to-end system that translates audio from English TED talks to German text, without producing intermediate English text. We use the S-Transformer architecture and train using the MuSTC dataset. We also describe several additional experiments that were attempted, but did not yield improved results.- Anthology ID:
- 2020.iwslt-1.6
- Volume:
- Proceedings of the 17th International Conference on Spoken Language Translation
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Marcello Federico, Alex Waibel, Kevin Knight, Satoshi Nakamura, Hermann Ney, Jan Niehues, Sebastian Stüker, Dekai Wu, Joseph Mariani, Francois Yvon
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 69–72
- Language:
- URL:
- https://aclanthology.org/2020.iwslt-1.6
- DOI:
- 10.18653/v1/2020.iwslt-1.6
- Cite (ACL):
- Arkady Arkhangorodsky, Yiqi Huang, and Amittai Axelrod. 2020. DiDi Labs’ End-to-end System for the IWSLT 2020 Offline Speech TranslationTask. In Proceedings of the 17th International Conference on Spoken Language Translation, pages 69–72, Online. Association for Computational Linguistics.
- Cite (Informal):
- DiDi Labs’ End-to-end System for the IWSLT 2020 Offline Speech TranslationTask (Arkhangorodsky et al., IWSLT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.iwslt-1.6.pdf